Abstract: A computer-implemented method for improving a predictive response of a machine learning-based virtual dialogue agent includes identifying an unfilled dialogue slot associated with an active dialogue between a user and the machine learning-based virtual dialogue agent, obtaining subsequent dialogue input data based on prompting the user for the unfilled dialogue slot, and computing a value of the unfilled dialogue slot based on the obtaining of the subsequent dialog input, wherein computing the value of the unfilled dialogue slot includes computing, via a question-answering machine learning model, a slot answer inference comprising the value of the unfilled dialogue slot based on an input of a machine learning-derived query and machine learning-derived context computed for the unfilled dialogue slot.

Abstract: A system and method for improving a predictive accuracy of a machine learning-based virtual conversational agent that includes computing an antecedent context nexus based on query embeddings computed for a preceding query input by a user, wherein the antecedent context nexus includes a pairing of a categorial parameter and a sub-categorical parameter derived based on the query embeddings of the preceding query; creating search logic parameters based on the categorical parameter and the sub-categorical parameter of the antecedent context nexus; executing a context nexus-informed search of a corpus of structured data using at least the search logic parameters; extracting a response candidate from the corpus of structured data based on the execution of the context nexus-informed search; constructing a response to the preceding query based on the extracted response candidate; and returning, via a user interface, the response to the preceding query.